Tag: data collection

Generative AI – a technology wonder of modern times – has revolutionized our ability to create and innovate. It also promises to have a profound impact on every facet of our lives. Beyond the seemingly magical powers of ChatGPT, Bard, MidJourney, and others, the emergence of what’s known as RAG (Retrieval Augmented Generation) has opened the possibility of augmenting Large Language Models (LLMs) with domain-specific enterprise data and knowledge.

RAG and its many variants have emerged as a pivotal technique in the realm of applied generative AI, improving LLM reliability and trustworthiness. Most recently, a technique known as Graph RAG has been getting a lot of attention, as it allows generative AI models to be combined with knowledge graphs to provide context for more accurate outputs. But what are its components and can it live up to the hype?

What is Graph RAG and What’s All the Fuss About?

According to Gartner, Graph RAG is a technique to improve the accuracy, reliability and explainability of retrieval-augmented generation (RAG) systems. The approach uses knowledge graphs (KGs) to improve the recall and precision of retrieval, either directly by pulling facts from a KG or indirectly by optimizing other retrieval methods. The added context refines the search space of results, eliminating irrelevant information.

Graph RAG enhances traditional RAG by integrating KGs to retrieve information and, using ontologies and taxonomies, builds context around entities involved in the user query. This approach leverages the structured nature of graphs to organize data as nodes and relationships, enabling efficient and accurate retrieval of relevant information to LLMs for generating responses.

KGs, which are a collection of interlinked descriptions of concepts, entities, relationships, and events, put data in context via linking and semantic metadata and provide a framework for data integration, unification, analytics and sharing. Here, they act as the source of structured, domain-specific context and information, enabling a nuanced understanding and retrieval of interconnected, heterogeneous information. This enhances the context and depth of the retrieved information, which results in accurate and relevant responses to user queries. This is especially true for complex domain-specific topics that require a deeper, holistic understanding of summarized semantic concepts over large data collections.

To Know More, Read Full Article @ https://ai-techpark.com/graph-rags-precision-advantage/

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As cyberattacks advance in their sophistication and frequency, traditional cybersecurity defenders-the firewalls, antivirus software, even intrusion detection systems-are no longer sufficient in protecting companies. Organizations are bound to face advanced persistent threats (APTs), ransomware, as well as insider attacks in 2024 that often go undetected by automated detection tools. This makes proactive cybersecurity a dire necessity.

According to new research findings, the average amount of time taken before it is possible to detect a breach stands at more than 200 days, which is a very long window for cyberthieves to siphon sensitive data and cripple business operations.

This mainly occurs in B2B organizations operating within the finance, healthcare, and technology sectors, as these sectors are mainly characterized by sophisticated attackers seeking high-value data. However, the only solution is in cyber threat hunting-a proactive security approach aimed at detecting threats before they trigger damage.

In the guide here, we will cover the most important steps to implement a robust cyber threat hunting strategy tailored for 2024-overview of all the skills, processes, and technologies that will help in keeping your business safe.

What is Cyber Threat Hunting?

Cyber threat hunting is one of the proactive cyber security practice wherein the trained and well-equipped security analysts proactively search for hidden or undetected threats within an organization’s network.  While the traditional monitoring systems passively wait for alerts, the threat hunters search for malicious activity or a weakness that can be exploited.

Why It Matters in 2024

Today, the threat landscape for cyber defence is no longer passive but active detection. Attackers are continually evolving by attempting to evade detection with tactics like lateral movement, credential dumping, and fileless malware. Threat hunting becomes very critical in this approach since it looks beyond waiting for automated tools to flag an anomaly and instead hunts for and discovers sophisticated attacks made to evade traditional defenses.

Common Cyber Threats in 2024

Some of the prominent threats businesses will face in 2024 include the following:

Advanced Persistent Threats (APTs): Organized cyberattacks that siphon off data for long periods of time without being detected.

Ransomware: A ransomware attack encrypts a victim’s data and demands payment in lieu of providing decryption keys.

Insider Threats: It is an employee or contractor who intends to do evil or shows malacious carelessness in doing his duty that might lead to security breaches.

Zero-Day Exploits: In this case, attacks exploit vulnerabilities that have not been patched yet.

To Know More, Read Full Article @ https://ai-techpark.com/implementing-cybersecurity-threat-hunting/

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Data Governance and Security Trends in 2024

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In today’s fast-paced business landscape, data is not just an asset; it’s a cornerstone of strategic decision-making. For B2B companies, leveraging data effectively can create significant competitive advantages, enabling them to understand their customers better, streamline operations, and drive innovation. This article explores the importance of a robust data strategy and how businesses can harness data to outpace their competition.

The Value of a Strong Data Strategy in B2B

Why Data is the New Competitive Currency

As businesses increasingly rely on data to inform their decisions, it has become the new competitive currency. Companies that effectively harness data can unlock valuable insights that guide product development, enhance customer experiences, and optimize operational efficiency. For instance, consider how a leading B2B SaaS company used data analytics to analyze customer usage patterns, which led to the development of new features that directly addressed user needs, resulting in a significant boost in customer retention.

Aligning Data Strategy with Business Goals

A successful data strategy must align with the overarching business objectives. Organizations should ensure that their data initiatives are not just about collection but are focused on measurable outcomes. For example, a manufacturing company may set specific targets for reducing downtime by analyzing equipment performance data. By aligning data strategy with business goals, companies can demonstrate clear ROI and reinforce the value of data initiatives across the organization.

Key Components of a Robust Data Strategy

Data Collection and Management

Effective data collection is the foundation of any data strategy. B2B organizations must prioritize collecting relevant and high-quality data from diverse sources, such as customer interactions, market research, and internal processes. Additionally, centralized data storage solutions, such as data lakes or warehouses, can streamline data management and improve access across departments.

Implementing robust data governance is equally essential. Establishing clear policies on data usage, ownership, and security ensures that data remains accurate, reliable, and compliant with regulations. This not only enhances decision-making but also builds trust among stakeholders who rely on data for strategic insights.

In an era where data is a vital asset, developing a robust data strategy is crucial for B2B organizations seeking a competitive edge. By aligning data initiatives with business goals, implementing best practices, and leveraging advanced tools, companies can harness the power of data to drive growth, enhance customer experiences, and remain agile in a dynamic marketplace. Embracing a culture of data-driven decision-making will not only empower organizations to thrive but also position them as leaders in their industries.

To Know More, Read Full Article @ https://ai-techpark.com/data-strategy-competitive-advantage/

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The first fundamental of Artificial Intelligence is data, with the Machine Learning models that feed on the continuously growing collections of data of different types. However, as far as it is a very significant source of information, it can be fraught with problems such as privacy limitations, biases, and data scarcity. This is beneficial in removing the mentioned above hurdles to bring synthetic data as a revolutionary solution in the world of AI.

What is Synthetic Data?

Synthetic data can be defined as data that is not acquired through actual occurrences or interactions but rather created fake data. It is specifically intended to mimic the characteristics, behaviors and organizations of actual data without copying them from actual observations. Although there exist a myriad of approaches to generating synthetic data, its generation might use simple rule-based systems or even more complicated methods, such as Machine Learning based on GANs. It is aimed at creating datasets which are as close as possible to real data, yet not causing the problems connected with using actual data.

In addition to being affordable, synthetic data is flexible and can, therefore, be applied at any scale. It enables organizations to produce significant amounts of data for developing or modeling systems or to train artificial intelligence especially when actual data is scarce, expensive or difficult to source. In addition, it is stated that synthetic data can effectively eliminate privacy related issues in fields like health and finance, as it is not based on any real information, thus may be considered as a powerful tool for data-related projects. It also helps increase the model’s ability to handle various situations since the machine learning model encounters many different situations.

Why is Synthetic Data a Game-Changer?

Synthetic data calls for the alteration of traditional methods used in industries to undertake data-driven projects due to the various advantages that the use of synthetic data avails. With an increasing number of big, diverse, and high-quality datasets needed, synthetic data becomes one of the solutions to the real-world data gathering process, which can be costly, time-consuming, or/and unethical.  This artificial data is created in a closed environment and means that data scientists and organisations have the possibility to construct datasets which correspond to their needs.

Synthetic data is an extremely valuable data product for any organization that wants to adapt to the changing landscape of data usage. It not only address practical problems like data unavailability and affordability but also flexibility, conforming to ethical standards, and model resilience. With a rising pace of technology advancements, there is a possibility of synthetic data becoming integral to building better, efficient, and responsible AI & ML models.

To Know More, Read Full Article @ https://ai-techpark.com/synthetic-data-in-machine-learning/

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Optimizing Data Governance and Lineage

Data Trends IT Professionals Need in 2024

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In an era where data is heralded as the new oil, there’s an inconvenient truth that many organizations are just beginning to confront: it is therefore important to realize that not all data is equal. With the increasing digitalization of the economy and an imperative to increasingly rely on data in products and services, the focus has been traditionally on the sheer amount of data that can be gathered to feed analytics, provide clients with personalized experiences, and inform strategic actions. However, without this policy to embrace data quality and data lineage, even the strenuous data collection would result in disastrous results.

Let us take an example of a general merchandising retailer chain that, to sustain and overcome its competitors, started a large-scale acquisition-based customer loyalty campaign with help of their gigantic data warehouse. High expectations of the initiative and great investment to make it work reached a deadlock when the issue was revealed: the data behind the plan was unreliable. The promotions of the retailer were wrong since the wrong customers were being targeted, and this eroded the trust of the customers.

This is not an unusual case. In fact, all these issues will sound very familiar in most organizations, yet often with no realization regarding potential hidden costs in the form of poor data quality and a lack of understanding in terms of data lineage. If data is to become a true strategic resource, then organizations have got to go beyond what appears to be mere numbers and down traceability of data. Only then can they establish the much-needed trust in today’s world to answer the diversified needs of the customers and the regulating bodies.

The Hidden Truth About Data: It’s Only as Good as Its Quality

The question is: Who would not want to work with data? The truth is that data is full of errors, inconsistencies, and inaccuracies. Data quality is an issue that ultimately touches upon the decision-making process, organizational compliance, and customer trust.  Let’s consider the following:

For instance, consider a marketing team working on creating a marketing campaign that was based on customer information that might have been entered incorrectly or not updated for several years. The result? Incorrect targeting, resource expenditure, and perhaps the antagonizing of clients. It therefore underlines the significance of sound data—a factor that is relevant both in making decisions and in customer relations.

Key Elements of Data Quality:

Accuracy: The data used should be accurate and depict the true worth and facts.

Completeness: All necessary data should be included without any gaps, i.e., all important data must be there with no breaks in between.

Consistency: Data should not only be uniform with all the systems and reports of the company, but also the format used should be uniform.

Timeliness: Data should be in real-time, and this data should be accessible whenever it is required.

Validity: The attributes should be of the right format and within the right range.

To Know More, Read Full Article @ https://ai-techpark.com/data-quality-and-data-lineage-elevate-trust-and-reliability/

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man Potential with Augmented Intelligence

Boosting Human Capabilities with Augmented Intelligence

Supercharging Human Potential with Augmented Intelligence

Explore how augmented intelligence enhances human potential, driving innovation and productivity in the modern workforce.

The business landscape has been transformed by over 75% in the past few years with the help of numerous technologies. One such marvel is augmented intelligence, which has emerged as a potent ally for human users, enhancing business capabilities by up to 60%. This technology represents a synergy between human expertise and machine learning (ML), redefining how human intelligence approaches problem-solving, decision-making, and innovation. Studies show that businesses utilizing augmented intelligence have seen a 50% increase in efficiency and a 40% improvement in decision-making accuracy. However, amidst all the insights, it is essential to understand that augmented intelligence is not a solution that can be operated independently. It requires human oversight and intervention to help carefully orchestrate ethical considerations and ensure alignment with human values and ideals.

In today’s AI Tech Park article, we will explore the boundless potential of augmented intelligence in reshaping the future of business.

A Symbiotic Relationship with Organizations and Augmented Intelligence

Augmented intelligence focuses on enhancing human capabilities by combining creativity and design-making skills with artificial intelligence’s (AI) ability to process large sets of data in a few seconds. For instance, in the healthcare sector, AI filters through millions of medical records to assist doctors in diagnosing and treating patients more effectively, therefore not replacing doctors’ expertise but augmenting it. Further, AI automates repetitive tasks, allowing human users to tackle more complex and creative work, especially with chatbots as they handle routine inquiries in customer service, allowing human agents to resolve more minute issues.

Augment intelligence uses personalized experience at a scale that informs users about current market trends, enhancing customer satisfaction, further helping to stimulate human creativity, and exploring new patterns and ideas. Numerous tools, such as OpenAI’s GPT-4 and Google Gemini, can create high-quality written content, which will assist writers and marketers in inefficiently generating social media posts and creative writing pieces. In terms of designing, genAI tools such as DALL-E and MidJourney work as guides that enable designers to generate unique images and artwork based on a few textual descriptions.

The human-AI collaboration offers potential by leveraging the strengths of both human creativity and augmented intelligence to achieve shared objectives of better business operations. However, the implementation of this technology doesn’t imply the replacement of human intelligence, but this collaborative initiative will enhance decision-making, boost efficiency, and transform business interaction to enhance organization scalability and personalization.

To Know More, Read Full Article @ https://ai-techpark.com/human-potential-with-augmented-intelligence/ 

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Top 5 Data Science Certifications

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As we have stepped into the digital world, data science is one of the most emerging technologies in the IT industry, as it aids in creating models that are trained on past data and are used to make data-driven decisions for the business.

With time, IT companies can understand the importance of data literacy and security and are eager to hire data professionals who can help them develop strategies for data collection, analysis, and segregation. So learning the appropriate data science skills is equally important for budding and seasoned data scientists to earn a handsome salary and also stay on top of the competition.

In this article, we will explore the top 10 data science certifications that are essential for budding or seasoned data scientists to build a strong foundation in this field.

Data Science Council of America (DASCA) Senior Data Scientist (SDS)

The Data Science Council of America’s (DASCA) Senior Data Scientist (SDS) certification program is designed for data scientists with five or more years of professional experience in data research and analytics. The program focuses on qualified knowledge of databases, spreadsheets, statistical analytics, SPSS/SAS, R, quantitative methods, and the fundamentals of object-oriented programming and RDBMS. This data science program has five trackers that will rank the candidates and track their requirements in terms of their educational and professional degree levels.

IBM Data Science Professional Certificate

The IBM Data Science Professional Certificate is an ideal program for data scientists who started their careers in the data science field. This certification consists of a series of nine courses that will help you acquire skills such as data science, open source tools, data science methodology, Python, databases and SQL, data analysis, data visualization, and machine learning (ML). By the end of the program, the candidates will have numerous assignments and projects to showcase their skills and enhance their resumes.

Open Certified Data Scientist (Open CDS)

The Open Group Professional Certification Program for the Data Scientist Professional (Open CDS) is an experienced certification program for candidates who are looking for an upgrade in their data science skills. The programs have three main levels: level one is to become a Certified Data Scientist; level two is to acquire a Master’s Certified Data Scientist; and the third level is to become a Distinguished Certified. This course will allow data scientists to earn their certificates and stay updated about new data trends.

Earning a certification in data science courses and programs is an excellent way to kickstart your career in data science and stand out from the competition. However, before selecting the correct course, it is best to consider which certification type is appropriate according to your education and job goals.

To Know More, Read Full Article @ https://ai-techpark.com/top-5-data-science-certifications-to-boost-your-skills/ 

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During the holiday season, consumer expectations evolve at a breakneck speed, the constant change in algorithms is reshaping the digital terrain, and data has become the new gold. It can be a challenging task for Chief Marketing Officers (CMOs) to handle the upcoming turbulence in the ever-evolving business landscape. So, amid this turbulence, a powerful tool that emerges as an ally is artificial intelligence (AI).

AI has taken center stage to understand and aid the demands of marketing leaders and business owners to stay ahead of the curve and ensure a long-term successful business. However, it is essential to future-proof the marketing strategies before we head into the upcoming years.

This article will be a strategic guide to future-proofing your marketing strategies with the help of AI.

2024 AI-powered Marketing Strategies

Marketing leaders believe that in 2024, the marketing landscape will likely continue evolving, with new trends and strategies emerging. Let’s take a glance at some AI-powered marketing strategies that CMOs might find valuable:

Interactive Content

Engaging content is one of the easiest ways to interact with users, as it tends to grab more attention and shares, which often keeps them hooked on your website. Examples of interactive content include polls, AR/VR experiences, quizzes, and shoppable posts.

AI-generated Video Marketing

Video is one of the most dominant forms of marketing, as users are more interested in watching the product than hearing about it. AI-generated visuals in the form of short videos, live streams, and interactive videos capture the audience’s attention and convey the correct message.

Influencer Marketing Platforms

Influencer marketing is steadily rising as marketers believe that it helps to upscale their brand through marketing campaigns by 67% in comparison to not having AI influencer marketing platforms. These AI influencer marketing platforms provide AI-driven analytics to users to get valuable insights into audience demographics, engagement rates, and ROI.

Privacy-Centric Marketing

The foundation of contemporary marketing is ethically trained AI, which provides unmatched accuracy in customer-centric monetization and communication. Businesses need to give user data protection a priority in light of growing privacy concerns. It will be essential to employ tactics like transparent data practices and zero-party data collection, which is the collection of data directly from customers.

To Know More, Read Full Article @ https://ai-techpark.com/marketing-strategies-with-ai/

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